Evidence map›Paper›PMID 42279632›Full record

ArticleFoods (Basel, Switzerland)2026

Optimization of New Cropland Allocation to Enhance Stable Utilization Potential: A Case Study of Guangdong Province, China.

Lesong Zhao, Ziyuan Qiao, Guangsheng Liu, Hongmei Wang

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lesong ZhaoSchool of Public Administration, South China Agricultural University, Guangzhou 510642, China.
Ziyuan QiaoSchool of Public Administration, South China Agricultural University, Guangzhou 510642, China.
Guangsheng LiuSchool of Public Administration, South China Agricultural University, Guangzhou 510642, China.
Hongmei WangSchool of Public Administration, South China Agricultural University, Guangzhou 510642, China.

Funding

National Social Science Fund of China No. 23CGL064
6 · The paper itself

Abstract

The optimization of new cropland allocation is crucial for promoting the efficient use of cropland resources and safeguarding food security. However, existing studies primarily take suitability as the optimization objective and lack the consideration of stable utilization potential, which may lead to subsequent unstable use. To address this gap, this study quantified the stable utilization potential of new cropland using a machine learning model and integrated it with the ant colony optimization (ACO) model to develop a spatial allocation framework. This framework was validated in Guangdong Province, China, a region characterized by diverse resource endowments and pronounced regional heterogeneity. The results indicated that, under the specified objective-weighting scheme and compared with the business-as-usual (BAU) scenario, the optimized scenario achieved regional cropland quantity balance. It also increased the average stable utilization potential of new cropland and overall utility by 34.84% and 12.74%, respectively, while reducing the mean cost and mean ecological benefit loss per unit area by 10.22% and 41.36%, respectively. Overall, under the specified constraints, the proposed framework offers a promising approach for new cropland planning and provides a basis for governments and land management authorities to improve future allocation practice.

Indexed as

Guangdong Provincenew croplandoptimized allocationscenario comparisonstable utilization potential

Identifiers

PMID42279632
PMCPMC13257041

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